Energy expenditure is a key component of energetics, and physical activity comprises the largest modifiable component of energy expenditure. Energy expenditure and physical activity are strongly related to insulin resistance and other markers of glycemic control important for cancer risk. Sedentary behavior has also recently emerged as an independent predictor of metabolic risk, and temporal analyses of objective sedentary behavior data have indicated that breaks in sitting time may be a critical intervention strategy to complement improvements in moderate to vigorous physical activity. In the last decade, the impact of the built environment has also been assessed in relation to physical activity, sedentary behavior and weight status. This research, however, has focused on a static view of residential neighborhood which may be confounding the relationship between health and place. We propose to advance the field of energy expenditure, physical activity, and sedentary behavior assessment across the cancer continuum by improving the accuracy of energy expenditure-related assessments in our TREC projects #2 and #3. We will use state ofthe art acceierometers with simultaneous heart rate recording to improve the accuracy of measuring physical activity, sedentary behavior, and energy expenditure. In addition to branched equation modeling techniques we will also use new computational approaches for analyzing data streams from these devices, including artificial neural networks that allow comtjining these data to decipher the frequency,intensity, duration, and type of physical activity and sedentary behavior so as to optimally characterize behaviors of study participants and reduce the measurement noise in observed relationships between these behaviors and markers of glycemic control. Finally, data from Global Positioning System devices that track the temporal and spatial movements of participants will be combined with existing Geographic Information Systems data for San Diego County to allow us to develop obesogenic environmental exposure estimates and relate these to the metabolic risk factors. These data will be processed through software developed by our group under the NIH Gene &Environment Initiative. This will enable us to use novel computational techniques to assess the relationships over time and across the study arms between energy expenditure, physical activity and sedentary behavior and metabolic risk factors related to breast cancer measured in Projects #2 &#3 as well as the moderating effect of exposure to obesogenic environments.